Data Summary /EDA {#eda} - Keana

Our dataset originates from the most recent (updated in 2015) version of the Canadian Nutrient File. The database contains average values for nutrients in foods available in Canada. These averages are based on the generic versions of a food, unless there is a brand specifically included in the database. This is a bilingual dataset with food names, descriptions, and background information that are in both French and English. One of the major goals in creating this version of the dataset was to update nutrient values for foods that are the largest contributors of sodium to the diet, since one of the major goals of manufacturers is to reduce sodium content of foods.

To this end, the database assesses more than 5690 unique foods, ranging from foods such as Cheese souffle to Vanilla extract, and provides the average nutrient levels per 100 grams.

Notably, some of the nutrients included are subcomponents of other nutrients. Or, like the two metrics for food energy (i.e., kcal and kJ), they are just different ways of measuring the same thing (i.e., one kcal = 4.184 kJ)

Also, it is worth noting that there are many values that are missing. For instance, only 0.982% of the rows have values for biotin (see Missing values in clean dataset section)

The original dataset was a relational dataset, with unique identifiers for the main variables of interest. Therefore, we had to merge on the unique identifiers (e.g., FoodID) across the relational datasets using the left_join function. Then, we

The original dataset was cleaned for analyses in the following ways:

(see here for a full summary of the variables in the cleaned dataset). All of the subsequent summary statistics and analyses mentioned in the paper will be using the clean dataset.

notes from here: chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/file:///C:/Users/keana/OneDrive%20-%20PennO365/Comp_transfer2018/Penn/fourth_yr/nutrients_project_stat571/cnf-fcen-csv/CNF%202015%20users_guide%20EN.pdf

description of file contents: From FOOD NAME.csv: FoodID (merging var) FoodGroupID (merging var) FoodDescription From NUTRIENT NAME.csv: NutrientID (merging var) NutrientName NutrientUnit (possibly as control??) From NUTRIENT AMOUNT.csv: NutrientValue FoodID (merging var) NutrientID (merging var) From FOOD GROUP.csv FoodGroupID (merging var) FoodGroupName

Main file of interest is NUTRIENT AMOUNT.csv – contains long version of dataset, where there are multiple rows for each food, along with a col for the nutrient identifier and the nutrient value associated with that identifier

Will have to combine the multiple datasets in one to have all info in one place

“At present foods are grouped under 23 different group headings based on similar characteristics of the foods”

The foodnamesare only available in this version in one lengthwhich does not include abbreviationsand can be up to 255 characters long

• All of the nutrient data is stored per 100g of the food (edible portion) - that is, all nutritional data is on the same scale

• in cleaning - only selected variables of interest (e.g., we removed mean SE for the nutrient values, since there were many missing values)

Analyses {#analyses} - Jeesung (both describing the analyses that were run - aka methods and running the analyses)

As mentioned before, there are several variables with multiple NA values. Therefore, to be able to run our target analyses, we needed to remove the NA values. We explored two different options for removing NA variables to see whether they would affect the results: mean imputation for all variables or first removing variables with more than 50% of NAs and then using mean imputation on the survived variables.

We found that the options produced similar results across our different options for mean imputation, so we used the version of the dataset where we used mean imputation for all variables in all subsequent analyses.

Since our goal is to identify the best algorithm for grouping foods that will help a person plan out their meals to best suit their needs, we compared different approaches to grouping the foods based on their nutrient profiles. That is, we wanted to maximize within-group similarity, while also minimizing between-group similarity, in nutrient profiles across different food groups. The first option we explored to achieve this goal was by applying k-means clustering to both versions of the dataset using the silhouette method to identify the optimal number of clusters. (briefly EXPLAIN LOGIC BEHIND THIS METHOD).

We will run spectrum clustering to see which foods are grouped together based on their nutrient profile. In doing so, we want to find out whether we can find clear-cut clusters of foods based on nutrition profile regardless of actual food group assignment. Our grouping results can be used to find the list of foods that contain the best combination of nutrients based on a persons dietary needs. This improves upon diets that try to rely on the actual food grouping, since our grouping result will be a better representation of the nutrient profile of a set of foods than the food group label, which may be determined arbitrarily. Since we have large dimensions of data (153 nutrient information for 5690 unique foods), we are going to run principal component analysis (PCA) to

Then, ran analysis on theoretically-driven set of nutrients (based on WHO).

1. Data Preparation

2. Data Analyses

## [1] "Size of each cluster is" "9"                      
## [3] "21"                      "9"                      
## [5] "23"                      "97"                     
## [7] "1785"                    "3687"                   
## [9] "59"

## [1] 67.8

## [1] 2.70e+08 2.39e+09 1.33e+09 1.57e+09 3.98e+09 3.22e+09 4.11e+09 5.92e+08

## [1] 51

## [1]  8357 16634 11440 62118  9362 14693 31793 21739

##  [1] Babyfood, snack, biscuit, mixed grain                     
##  [2] Egg, chicken, whole, fresh or frozen, raw                 
##  [3] Cream, table (coffee), 18% M.F.                           
##  [4] Spices, sage, ground                                      
##  [5] Guinea, meat and skin, raw                                
##  [6] Vegetable oil, palm                                       
##  [7] Fish oil, sardine                                         
##  [8] Cheese, Mexican, queso chihuahua                          
##  [9] Duck, domesticated, meat and skin, raw                    
## [10] Chicken, broiler, leg, meat and skin, batter dipped, fried
## 5689 Levels: Abiyuch, raw ... Zwieback

##  [1] Turkey, all classes, meat and skin, raw              
##  [2] Chicken, broiler, separable fat, raw                 
##  [3] Pear, dried halves, sulphured, cooked, no added sugar
##  [4] Shortening, industrial, for baking (cake), soybean   
##  [5] Pork, loin, tenderloin, lean and fat, broiled        
##  [6] Brussels sprouts, frozen, boiled, drained            
##  [7] Soup, chicken noodle, canned, condensed, water added 
##  [8] Turkey, tom, meat and skin, raw                      
##  [9] Cress, garden, boiled, drained                       
## [10] Egg substitute, frozen (yolk replaced)               
## 5689 Levels: Abiyuch, raw ... Zwieback

EXPLORATORY ANALYSES: REMOVE OVERLAPPING VARIABLES (CAN GO TO APPENDIX)

• from the main analyses above, we found “FAT” information is overexaggerated when scaling is applied
• only include “TOTAL FAT” and removed all subtypes of FAT

1. Data Preparation

2. Data Analyses

## [1] "Size of each cluster is" "110"                    
## [3] "19"                      "67"                     
## [5] "30"                      "5464"

Full summary of clean dataset

Data Frame Summary

wide_data

Dimensions: 5690 x 154
Duplicates: 0

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	FoodGroupName [factor]	1. Babyfoods 2. Baked Products 3. Beef Products 4. Beverages 5. Breakfast cereals 6. Cereals, Grains and Pasta 7. Dairy and Egg Products 8. Fast Foods 9. Fats and Oils 10. Finfish and Shellfish Pro [ 13 others ]	94 ( 1.7%) 441 ( 7.8%) 170 ( 3.0%) 243 ( 4.3%) 212 ( 3.7%) 155 ( 2.7%) 241 ( 4.2%) 174 ( 3.1%) 144 ( 2.5%) 325 ( 5.7%) 3491 (61.4%)		0 (0.0%)
2	PROTEIN [numeric]	Mean (sd) : 11.1 (10.8) min < med < max: 0 < 7.6 < 85.6 IQR (CV) : 16.6 (1)	2261 distinct values		0 (0.0%)
3	FAT (TOTAL LIPIDS) [numeric]	Mean (sd) : 10 (16.7) min < med < max: 0 < 3.8 < 100 IQR (CV) : 11.6 (1.7)	1913 distinct values		0 (0.0%)
4	CARBOHYDRATE, TOTAL (BY DIFFERENCE) [numeric]	Mean (sd) : 22 (26.5) min < med < max: 0 < 10.3 < 100 IQR (CV) : 31.6 (1.2)	2756 distinct values		0 (0.0%)
5	ASH, TOTAL [numeric]	Mean (sd) : 1.9 (3.5) min < med < max: 0 < 1.2 < 99.8 IQR (CV) : 1.2 (1.8)	646 distinct values		1 (0.0%)
6	ENERGY (KILOCALORIES) [numeric]	Mean (sd) : 219 (174) min < med < max: 0 < 174 < 902 IQR (CV) : 240 (0.8)	665 distinct values		0 (0.0%)
7	ALCOHOL [numeric]	Mean (sd) : 0.1 (1.8) min < med < max: 0 < 0 < 42.5 IQR (CV) : 0 (14.7)	37 distinct values		325 (5.7%)
8	MOISTURE [numeric]	Mean (sd) : 55 (31) min < med < max: 0 < 64.7 < 100 IQR (CV) : 50.3 (0.6)	3417 distinct values		0 (0.0%)
9	CAFFEINE [numeric]	Mean (sd) : 3.9 (101) min < med < max: 0 < 0 < 5714 IQR (CV) : 0 (25.6)	61 distinct values		312 (5.5%)
10	THEOBROMINE [numeric]	Mean (sd) : 7 (77.1) min < med < max: 0 < 0 < 2634 IQR (CV) : 0 (11.1)	130 distinct values		338 (5.9%)
11	ENERGY (KILOJOULES) [numeric]	Mean (sd) : 915 (727) min < med < max: 0 < 727 < 3774 IQR (CV) : 1006 (0.8)	1658 distinct values		1 (0.0%)
12	SUGARS, TOTAL [numeric]	Mean (sd) : 7.7 (15) min < med < max: 0 < 1.3 < 99.8 IQR (CV) : 7.6 (1.9)	1505 distinct values		1046 (18.4%)
13	FIBRE, TOTAL DIETARY [numeric]	Mean (sd) : 2.4 (4.8) min < med < max: 0 < 0.8 < 79 IQR (CV) : 2.8 (2)	237 distinct values		224 (3.9%)
14	CALCIUM [numeric]	Mean (sd) : 76.9 (220) min < med < max: 0 < 24 < 7364 IQR (CV) : 60 (2.9)	468 distinct values		51 (0.9%)
15	IRON [numeric]	Mean (sd) : 2.6 (5.6) min < med < max: 0 < 1.1 < 124 IQR (CV) : 2.1 (2.2)	882 distinct values		51 (0.9%)
16	MAGNESIUM [numeric]	Mean (sd) : 39.7 (64.8) min < med < max: 0 < 21 < 781 IQR (CV) : 23 (1.6)	302 distinct values		214 (3.8%)
17	PHOSPHORUS [numeric]	Mean (sd) : 168 (236) min < med < max: 0 < 130 < 9918 IQR (CV) : 176 (1.4)	620 distinct values		153 (2.7%)
18	POTASSIUM [numeric]	Mean (sd) : 308 (447) min < med < max: 0 < 232 < 16500 IQR (CV) : 215 (1.5)	895 distinct values		165 (2.9%)
19	SODIUM [numeric]	Mean (sd) : 333 (1219) min < med < max: 0 < 82 < 38758 IQR (CV) : 339 (3.7)	1099 distinct values		43 (0.8%)
20	ZINC [numeric]	Mean (sd) : 1.6 (3) min < med < max: 0 < 0.8 < 91 IQR (CV) : 1.8 (1.9)	695 distinct values		220 (3.9%)
21	COPPER [numeric]	Mean (sd) : 0.2 (0.6) min < med < max: 0 < 0.1 < 15.1 IQR (CV) : 0.2 (2.8)	787 distinct values		270 (4.7%)
22	MANGANESE [numeric]	Mean (sd) : 0.6 (3.7) min < med < max: 0 < 0.1 < 133 IQR (CV) : 0.4 (6.1)	1226 distinct values		585 (10.3%)
23	SELENIUM [numeric]	Mean (sd) : 14.6 (36.7) min < med < max: 0 < 6.9 < 1917 IQR (CV) : 19.4 (2.5)	614 distinct values		722 (12.7%)
24	RETINOL [numeric]	Mean (sd) : 88.8 (840) min < med < max: 0 < 0 < 30000 IQR (CV) : 11 (9.5)	326 distinct values		499 (8.8%)
25	BETA CAROTENE [numeric]	Mean (sd) : 292 (1711) min < med < max: 0 < 0 < 42891 IQR (CV) : 33 (5.9)	612 distinct values		653 (11.5%)
26	ALPHA-TOCOPHEROL [numeric]	Mean (sd) : 1.2 (4.1) min < med < max: 0 < 0.3 < 149 IQR (CV) : 0.6 (3.5)	447 distinct values		1555 (27.3%)
27	VITAMIN D (INTERNATIONAL UNITS) [numeric]	Mean (sd) : 23.9 (241) min < med < max: 0 < 0 < 12716 IQR (CV) : 6 (10.1)	214 distinct values		692 (12.2%)
28	VITAMIN D (D2 + D3) [numeric]	Mean (sd) : 0.6 (6.3) min < med < max: 0 < 0 < 318 IQR (CV) : 0.2 (10)	129 distinct values		690 (12.1%)
29	VITAMIN C [numeric]	Mean (sd) : 8.2 (53.2) min < med < max: 0 < 0.1 < 1900 IQR (CV) : 3.6 (6.5)	458 distinct values		184 (3.2%)
30	THIAMIN [numeric]	Mean (sd) : 0.2 (0.6) min < med < max: 0 < 0.1 < 23.4 IQR (CV) : 0.2 (2.6)	812 distinct values		280 (4.9%)
31	RIBOFLAVIN [numeric]	Mean (sd) : 0.2 (0.5) min < med < max: 0 < 0.1 < 17.5 IQR (CV) : 0.2 (2.1)	709 distinct values		261 (4.6%)
32	NIACIN (NICOTINIC ACID) PREFORMED [numeric]	Mean (sd) : 3.1 (4.4) min < med < max: 0 < 1.6 < 128 IQR (CV) : 4.4 (1.4)	2828 distinct values		234 (4.1%)
33	TOTAL NIACIN EQUIVALENT [numeric]	Mean (sd) : 5.2 (5.6) min < med < max: 0 < 3.5 < 132 IQR (CV) : 7.2 (1.1)	3908 distinct values		234 (4.1%)
34	PANTOTHENIC ACID [numeric]	Mean (sd) : 0.6 (0.9) min < med < max: 0 < 0.4 < 21.9 IQR (CV) : 0.7 (1.5)	1316 distinct values		936 (16.4%)
35	VITAMIN B-6 [numeric]	Mean (sd) : 0.2 (1) min < med < max: 0 < 0.1 < 68.8 IQR (CV) : 0.2 (4.4)	756 distinct values		397 (7.0%)
36	TOTAL FOLACIN [numeric]	Mean (sd) : 37.7 (93.4) min < med < max: 0 < 12 < 3786 IQR (CV) : 35 (2.5)	290 distinct values		408 (7.2%)
37	VITAMIN B-12 [numeric]	Mean (sd) : 1.1 (6.8) min < med < max: 0 < 0 < 380 IQR (CV) : 0.7 (6.1)	899 distinct values		354 (6.2%)
38	VITAMIN K [numeric]	Mean (sd) : 20.8 (99.9) min < med < max: 0 < 1.7 < 1714 IQR (CV) : 6 (4.8)	434 distinct values		2516 (44.2%)
39	FOLIC ACID [numeric]	Mean (sd) : 8.4 (49.1) min < med < max: 0 < 0 < 2993 IQR (CV) : 0 (5.8)	160 distinct values		160 (2.8%)
40	TRYPTOPHAN [numeric]	Mean (sd) : 0.1 (0.1) min < med < max: 0 < 0.1 < 1.6 IQR (CV) : 0.2 (0.9)	458 distinct values		1835 (32.2%)
41	THREONINE [numeric]	Mean (sd) : 0.5 (0.5) min < med < max: 0 < 0.3 < 3.7 IQR (CV) : 0.8 (0.9)	1300 distinct values		1782 (31.3%)
42	ISOLEUCINE [numeric]	Mean (sd) : 0.6 (0.5) min < med < max: 0 < 0.4 < 5 IQR (CV) : 0.8 (0.9)	1369 distinct values		1778 (31.2%)
43	LEUCINE [numeric]	Mean (sd) : 1 (0.9) min < med < max: 0 < 0.7 < 7.2 IQR (CV) : 1.4 (0.9)	1834 distinct values		1782 (31.3%)
44	LYSINE [numeric]	Mean (sd) : 0.9 (0.9) min < med < max: 0 < 0.4 < 5.8 IQR (CV) : 1.6 (1)	1698 distinct values		1764 (31.0%)
45	METHIONINE [numeric]	Mean (sd) : 0.3 (0.3) min < med < max: 0 < 0.2 < 3.2 IQR (CV) : 0.5 (1)	859 distinct values		1767 (31.1%)
46	CYSTINE [numeric]	Mean (sd) : 0.2 (0.2) min < med < max: 0 < 0.1 < 2.1 IQR (CV) : 0.2 (1)	495 distinct values		1842 (32.4%)
47	PHENYLALANINE [numeric]	Mean (sd) : 0.5 (0.5) min < med < max: 0 < 0.5 < 5.2 IQR (CV) : 0.7 (0.9)	1274 distinct values		1782 (31.3%)
48	TYROSINE [numeric]	Mean (sd) : 0.4 (0.4) min < med < max: 0 < 0.3 < 3.3 IQR (CV) : 0.6 (0.9)	1131 distinct values		1811 (31.8%)
49	VALINE [numeric]	Mean (sd) : 0.6 (0.6) min < med < max: 0 < 0.4 < 6.2 IQR (CV) : 0.9 (0.9)	1428 distinct values		1778 (31.2%)
50	ARGININE [numeric]	Mean (sd) : 0.8 (0.8) min < med < max: 0 < 0.5 < 7.4 IQR (CV) : 1.2 (1)	1626 distinct values		1791 (31.5%)
51	HISTIDINE [numeric]	Mean (sd) : 0.4 (0.4) min < med < max: 0 < 0.2 < 2.3 IQR (CV) : 0.6 (1)	1075 distinct values		1784 (31.4%)
52	ALANINE [numeric]	Mean (sd) : 0.7 (0.7) min < med < max: 0 < 0.4 < 8 IQR (CV) : 1 (1)	1489 distinct values		1836 (32.3%)
53	ASPARTIC ACID [numeric]	Mean (sd) : 1.2 (1.1) min < med < max: 0 < 0.8 < 10.2 IQR (CV) : 1.7 (0.9)	1936 distinct values		1850 (32.5%)
54	GLUTAMIC ACID [numeric]	Mean (sd) : 2.4 (12.3) min < med < max: 0 < 1.9 < 757 IQR (CV) : 2.9 (5.2)	2432 distinct values		1833 (32.2%)
55	GLYCINE [numeric]	Mean (sd) : 0.6 (0.7) min < med < max: 0 < 0.4 < 19 IQR (CV) : 1 (1.1)	1438 distinct values		1835 (32.2%)
56	PROLINE [numeric]	Mean (sd) : 0.7 (0.6) min < med < max: 0 < 0.6 < 12.3 IQR (CV) : 0.8 (1)	1419 distinct values		1843 (32.4%)
57	SERINE [numeric]	Mean (sd) : 0.5 (0.5) min < med < max: 0 < 0.5 < 6.1 IQR (CV) : 0.7 (0.9)	1288 distinct values		1844 (32.4%)
58	CHOLESTEROL [numeric]	Mean (sd) : 41.5 (138) min < med < max: 0 < 1 < 3100 IQR (CV) : 61 (3.3)	291 distinct values		194 (3.4%)
59	FATTY ACIDS, TRANS, TOTAL [numeric]	Mean (sd) : 0.3 (1.7) min < med < max: 0 < 0 < 37.6 IQR (CV) : 0.2 (5.9)	498 distinct values		3559 (62.5%)
60	FATTY ACIDS, SATURATED, TOTAL [numeric]	Mean (sd) : 3.1 (5.8) min < med < max: 0 < 1.1 < 95.6 IQR (CV) : 3.5 (1.9)	2812 distinct values		238 (4.2%)
61	FATTY ACIDS, SATURATED, 8:0, OCTANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 7.5 IQR (CV) : 0 (7.1)	266 distinct values		1668 (29.3%)
62	FATTY ACIDS, SATURATED, 10:0, DECANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 6 IQR (CV) : 0 (5.1)	350 distinct values		1364 (24.0%)
63	FATTY ACIDS, SATURATED, 12:0, DODECANOIC [numeric]	Mean (sd) : 0.2 (1.7) min < med < max: 0 < 0 < 47 IQR (CV) : 0 (8.7)	448 distinct values		1201 (21.1%)
64	FATTY ACIDS, SATURATED, 14:0, TETRADECANOIC [numeric]	Mean (sd) : 0.2 (0.9) min < med < max: 0 < 0 < 22.8 IQR (CV) : 0.2 (3.5)	787 distinct values		788 (13.8%)
65	FATTY ACIDS, SATURATED, 16:0, HEXADECANOIC [numeric]	Mean (sd) : 1.7 (2.8) min < med < max: 0 < 0.7 < 43.5 IQR (CV) : 2.1 (1.7)	2322 distinct values		602 (10.6%)
66	FATTY ACIDS, SATURATED, 18:0, OCTADECANOIC [numeric]	Mean (sd) : 0.8 (1.7) min < med < max: 0 < 0.3 < 33.2 IQR (CV) : 0.9 (2)	1675 distinct values		615 (10.8%)
67	FATTY ACIDS, MONOUNSATURATED, 18:1undifferentiated, OCTADECENOIC [numeric]	Mean (sd) : 3.5 (7.2) min < med < max: 0 < 1 < 82.6 IQR (CV) : 4 (2)	2708 distinct values		578 (10.2%)
68	FATTY ACIDS, POLYUNSATURATED, 18:2undifferentiated, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 1.8 (4.7) min < med < max: 0 < 0.4 < 74.6 IQR (CV) : 1.5 (2.6)	2079 distinct values		561 (9.9%)
69	FATTY ACIDS, POLYUNSATURATED, 18:3undifferentiated, LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0.2 (1.2) min < med < max: 0 < 0.1 < 53.4 IQR (CV) : 0.1 (6)	689 distinct values		656 (11.5%)
70	FATTY ACIDS, POLYUNSATURATED, 20:4, EICOSATETRAENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (2.6)	261 distinct values		1210 (21.3%)
71	FATTY ACIDS, POLYUNSATURATED, 22:6 n-3, DOCOSAHEXAENOIC (DHA) [numeric]	Mean (sd) : 0 (0.5) min < med < max: 0 < 0 < 18.2 IQR (CV) : 0 (9.6)	296 distinct values		137 (2.4%)
72	FATTY ACIDS, MONOUNSATURATED, 16:1undifferentiated, HEXADECENOIC [numeric]	Mean (sd) : 0.2 (1) min < med < max: 0 < 0 < 18.9 IQR (CV) : 0.2 (4)	770 distinct values		836 (14.7%)
73	FATTY ACIDS, POLYUNSATURATED, 18:4, OCTADECATETRAENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3 IQR (CV) : 0 (10.2)	126 distinct values		1781 (31.3%)
74	FATTY ACIDS, POLYUNSATURATED, 20:5 n-3, EICOSAPENTAENOIC (EPA) [numeric]	Mean (sd) : 0 (0.4) min < med < max: 0 < 0 < 13.2 IQR (CV) : 0 (9.7)	259 distinct values		1306 (23.0%)
75	FATTY ACIDS, MONOUNSATURATED, 22:1undifferentiated, DOCOSENOIC [numeric]	Mean (sd) : 0 (0.8) min < med < max: 0 < 0 < 41.2 IQR (CV) : 0 (17.3)	199 distinct values		1532 (26.9%)
76	FATTY ACIDS, POLYUNSATURATED, 22:5 n-3, DOCOSAPENTAENOIC (DPA) [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 5.6 IQR (CV) : 0 (11.1)	162 distinct values		149 (2.6%)
77	FATTY ACIDS, MONOUNSATURATED, TOTAL [numeric]	Mean (sd) : 3.9 (7.8) min < med < max: 0 < 1.2 < 83.7 IQR (CV) : 4.5 (2)	2880 distinct values		314 (5.5%)
78	FATTY ACIDS, POLYUNSATURATED, TOTAL [numeric]	Mean (sd) : 2.2 (5.2) min < med < max: 0 < 0.6 < 74.6 IQR (CV) : 1.8 (2.4)	2381 distinct values		316 (5.6%)
79	NATURALLY OCCURRING FOLATE [numeric]	Mean (sd) : 29.2 (75.3) min < med < max: 0 < 9 < 2340 IQR (CV) : 20 (2.6)	261 distinct values		503 (8.8%)
80	RETINOL ACTIVITY EQUIVALENTS [numeric]	Mean (sd) : 115 (836) min < med < max: 0 < 3 < 30000 IQR (CV) : 33 (7.3)	463 distinct values		260 (4.6%)
81	DIETARY FOLATE EQUIVALENTS [numeric]	Mean (sd) : 44.4 (119) min < med < max: 0 < 12 < 5881 IQR (CV) : 41 (2.7)	332 distinct values		499 (8.8%)
82	FATTY ACIDS, POLYUNSATURATED, 18:2 c,c n-6, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 2.3 (6) min < med < max: 0 < 0.5 < 74.6 IQR (CV) : 1.5 (2.6)	1285 distinct values		3256 (57.2%)
83	FATTY ACIDS, POLYUNSATURATED, 20:3, EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.4 IQR (CV) : 0 (7.1)	89 distinct values		2322 (40.8%)
84	FATTY ACIDS, POLYUNSATURATED, 18:3 c,c,c n-3 LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0.2 (1.3) min < med < max: 0 < 0 < 53.4 IQR (CV) : 0.1 (6.2)	619 distinct values		954 (16.8%)
85	FATTY ACIDS, POLYUNSATURATED, 18:3 c,c,c n-6, g-LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1 IQR (CV) : 0 (18.1)	49 distinct values		307 (5.4%)
86	BETA CRYPTOXANTHIN [numeric]	Mean (sd) : 15.2 (198) min < med < max: 0 < 0 < 6252 IQR (CV) : 0 (13)	128 distinct values		2334 (41.0%)
87	LYCOPENE [numeric]	Mean (sd) : 220 (1807) min < med < max: 0 < 0 < 46260 IQR (CV) : 0 (8.2)	190 distinct values		2324 (40.8%)
88	LUTEIN AND ZEAXANTHIN [numeric]	Mean (sd) : 260 (1387) min < med < max: 0 < 0 < 19697 IQR (CV) : 39 (5.3)	419 distinct values		2346 (41.2%)
89	FATTY ACIDS, POLYUNSATURATED, 20:3 n-6, EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.4 IQR (CV) : 0 (14.6)	71 distinct values		521 (9.2%)
90	FATTY ACIDS, POLYUNSATURATED, 20:4 n-6, ARACHIDONIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (2.7)	227 distinct values		2625 (46.1%)
91	FATTY ACIDS, POLYUNSATURATED, 20:3 n-3 EICOSATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1 IQR (CV) : 0 (16.7)	52 distinct values		505 (8.9%)
92	VITAMIN B12, ADDED [numeric]	Mean (sd) : 1 (17.5) min < med < max: 0 < 0 < 380 IQR (CV) : 0 (18.1)	28 distinct values		5218 (91.7%)
93	ALPHA-TOCOPHEROL, ADDED [numeric]	Mean (sd) : 0.1 (0.9) min < med < max: 0 < 0 < 16.9 IQR (CV) : 0 (12)	11 distinct values		5231 (91.9%)
94	VITAMIN D2, ERGOCALCIFEROL [numeric]	Mean (sd) : 0.3 (2) min < med < max: 0 < 0 < 28.1 IQR (CV) : 0 (6.3)	22 distinct values		5344 (93.9%)
95	FATTY ACIDS, SATURATED, 4:0, BUTANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 3.2 IQR (CV) : 0 (5)	274 distinct values		1839 (32.3%)
96	FATTY ACIDS, SATURATED, 6:0, HEXANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2 IQR (CV) : 0 (4.9)	224 distinct values		1816 (31.9%)
97	ALPHA CAROTENE [numeric]	Mean (sd) : 40.8 (387) min < med < max: 0 < 0 < 14251 IQR (CV) : 0 (9.5)	164 distinct values		2340 (41.1%)
98	FATTY ACIDS, MONOUNSATURATED, 22:1c, DOCOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (5.7)	100 distinct values		2912 (51.2%)
99	FATTY ACIDS, POLYUNSATURATED, 18:3i, LINOLENIC, OCTADECATRIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.3 IQR (CV) : 0 (4.7)	54 distinct values		4419 (77.7%)
100	FATTY ACIDS, MONOUNSATURATED, 22:1t, DOCOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (18.5)	16 distinct values		2983 (52.4%)
101	SUCROSE [numeric]	Mean (sd) : 2 (7.3) min < med < max: 0 < 0 < 99.8 IQR (CV) : 0.4 (3.7)	487 distinct values		3044 (53.5%)
102	GLUCOSE [numeric]	Mean (sd) : 0.8 (2.5) min < med < max: 0 < 0 < 35.8 IQR (CV) : 0.5 (3.2)	399 distinct values		3051 (53.6%)
103	FRUCTOSE [numeric]	Mean (sd) : 0.7 (2.5) min < med < max: 0 < 0 < 55.6 IQR (CV) : 0.3 (3.6)	387 distinct values		3055 (53.7%)
104	LACTOSE [numeric]	Mean (sd) : 0.3 (1.2) min < med < max: 0 < 0 < 13.2 IQR (CV) : 0 (4)	225 distinct values		3076 (54.1%)
105	MALTOSE [numeric]	Mean (sd) : 0.2 (0.8) min < med < max: 0 < 0 < 16.4 IQR (CV) : 0 (3.9)	217 distinct values		3098 (54.4%)
106	GALACTOSE [numeric]	Mean (sd) : 0 (0.5) min < med < max: 0 < 0 < 19.9 IQR (CV) : 0 (14.1)	53 distinct values		3122 (54.9%)
107	FATTY ACIDS, SATURATED, 20:0, EICOSANOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 4.6 IQR (CV) : 0 (4.6)	183 distinct values		3649 (64.1%)
108	FATTY ACIDS, SATURATED, 22:0, DOCOSANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3.7 IQR (CV) : 0 (5.8)	133 distinct values		3691 (64.9%)
109	FATTY ACIDS, MONOUNSATURATED, 14:1, TETRADECENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 1.8 IQR (CV) : 0 (4)	156 distinct values		3666 (64.4%)
110	FATTY ACIDS, MONOUNSATURATED, 20:1, EICOSENOIC [numeric]	Mean (sd) : 0.1 (0.6) min < med < max: 0 < 0 < 15 IQR (CV) : 0 (6.3)	365 distinct values		1759 (30.9%)
111	FATTY ACIDS, SATURATED, 15:0, PENTADECANOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.9 IQR (CV) : 0 (2.9)	121 distinct values		3772 (66.3%)
112	FATTY ACIDS, SATURATED, 17:0, HEPTADECANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 0.8 IQR (CV) : 0 (2)	189 distinct values		3723 (65.4%)
113	FATTY ACIDS, SATURATED, 24:0, TETRACOSANOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 4.7 IQR (CV) : 0 (9.4)	91 distinct values		3949 (69.4%)
114	STARCH [numeric]	Mean (sd) : 4 (11.4) min < med < max: 0 < 0 < 73.3 IQR (CV) : 0 (2.9)	360 distinct values		3755 (66.0%)
115	BETA-TOCOPHEROL [numeric]	Mean (sd) : 0.1 (0.5) min < med < max: 0 < 0 < 10.5 IQR (CV) : 0.1 (5.1)	65 distinct values		4929 (86.6%)
116	GAMMA-TOCOPHEROL [numeric]	Mean (sd) : 2.3 (5.7) min < med < max: 0 < 0.2 < 65.2 IQR (CV) : 1.7 (2.5)	274 distinct values		4922 (86.5%)
117	DELTA-TOCOPHEROL [numeric]	Mean (sd) : 0.4 (1.3) min < med < max: 0 < 0 < 15.4 IQR (CV) : 0.2 (3.2)	148 distinct values		4928 (86.6%)
118	FATTY ACIDS, MONOUNSATURATED, 16:1t, HEXADECENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 6.1 IQR (CV) : 0 (19.6)	73 distinct values		3959 (69.6%)
119	FATTY ACIDS, MONOUNSATURATED, 18:1t, OCTADECENOIC [numeric]	Mean (sd) : 0.1 (0.7) min < med < max: 0 < 0 < 20.2 IQR (CV) : 0.1 (5.6)	295 distinct values		4118 (72.4%)
120	FATTY ACIDS, POLYUNSATURATED, 18:2i, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2.3 IQR (CV) : 0 (3.6)	140 distinct values		4332 (76.1%)
121	FATTY ACIDS, MONOUNSATURATED, 24:1c, TETRACOSENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.6 IQR (CV) : 0 (7.9)	45 distinct values		4148 (72.9%)
122	FATTY ACIDS, MONOUNSATURATED, 16:1c, HEXADECENOIC [numeric]	Mean (sd) : 0.1 (0.3) min < med < max: 0 < 0 < 6.9 IQR (CV) : 0.1 (2.4)	396 distinct values		3923 (68.9%)
123	FATTY ACIDS, POLYUNSATURATED, 20:2 c,c EICOSADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.7 IQR (CV) : 0 (3.1)	128 distinct values		3854 (67.7%)
124	FATTY ACIDS, MONOUNSATURATED, 18:1c, OCTADECENOIC [numeric]	Mean (sd) : 4.7 (72.2) min < med < max: 0 < 1.1 < 2845 IQR (CV) : 3.2 (15.5)	1066 distinct values		4132 (72.6%)
125	FATTY ACIDS, MONOUNSATURATED, 17:1, HEPTADECENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (2.7)	135 distinct values		3903 (68.6%)
126	FATTY ACIDS, TOTAL TRANS-MONOENOIC [numeric]	Mean (sd) : 0.1 (0.7) min < med < max: 0 < 0 < 20.2 IQR (CV) : 0.1 (6.2)	285 distinct values		4249 (74.7%)
127	FATTY ACIDS, MONOUNSATURATED, 15:1, PENTADECENOIC [numeric]	Mean (sd) : 0 (0.2) min < med < max: 0 < 0 < 6 IQR (CV) : 0 (28)	27 distinct values		4050 (71.2%)
128	FATTY ACIDS, POLYUNSATURATED, CONJUGATED, 18:2 cla, LINOLEIC, OCTADECADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 1.1 IQR (CV) : 0 (4.4)	90 distinct values		4331 (76.1%)
129	FATTY ACIDS, POLYUNSATURATED, 22:4 n-6, DOCOSATETRAENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.3 IQR (CV) : 0 (3.1)	66 distinct values		4229 (74.3%)
130	FATTY ACIDS, TOTAL TRANS-POLYENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2.5 IQR (CV) : 0 (3.8)	154 distinct values		4330 (76.1%)
131	CHOLINE, TOTAL [numeric]	Mean (sd) : 39.1 (70.6) min < med < max: 0 < 19 < 2403 IQR (CV) : 50.5 (1.8)	910 distinct values		2813 (49.4%)
132	BETAINE [numeric]	Mean (sd) : 10.6 (31.5) min < med < max: 0 < 3.9 < 630 IQR (CV) : 10.1 (3)	257 distinct values		4589 (80.7%)
133	FATTY ACIDS, POLYUNSATURATED, TOTAL OMEGA N-3 [numeric]	Mean (sd) : 0.5 (2.4) min < med < max: 0 < 0.1 < 53.4 IQR (CV) : 0.2 (4.9)	548 distinct values		3717 (65.3%)
134	FATTY ACIDS, POLYUNSATURATED, TOTAL OMEGA N-6 [numeric]	Mean (sd) : 3.1 (23.3) min < med < max: 0 < 0.5 < 953 IQR (CV) : 1.4 (7.6)	1055 distinct values		3711 (65.2%)
135	ASPARTAME [numeric]	Mean (sd) : 51.1 (403) min < med < max: 0 < 0 < 3722 IQR (CV) : 0 (7.9)	0 : 82 (94.3%) 37 : 1 ( 1.1%) 42 : 1 ( 1.1%) 52 : 1 ( 1.1%) 597 : 1 ( 1.1%) 3722 : 1 ( 1.1%)		5603 (98.5%)
136	TOTAL PLANT STEROL [numeric]	Mean (sd) : 26.4 (80.7) min < med < max: 0 < 0 < 1190 IQR (CV) : 14 (3.1)	117 distinct values		4995 (87.8%)
137	MANNITOL [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.2 IQR (CV) : 0 (17.6)	3 distinct values		4313 (75.8%)
138	SORBITOL [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 2.3 IQR (CV) : 0 (14)	9 distinct values		4304 (75.6%)
139	STIGMASTEROL [numeric]	Mean (sd) : 1.3 (5.9) min < med < max: 0 < 0 < 59 IQR (CV) : 0 (4.6)	26 distinct values		5183 (91.1%)
140	TOTAL MONOSACCARIDES [numeric]	Mean (sd) : 0.8 (2.7) min < med < max: 0 < 0 < 30.6 IQR (CV) : 0.1 (3.3)	267 distinct values		3810 (67.0%)
141	TOTAL DISACCHARIDES [numeric]	Mean (sd) : 1.5 (4.8) min < med < max: 0 < 0 < 47.2 IQR (CV) : 0 (3.3)	295 distinct values		3824 (67.2%)
142	BETA-SITOSTEROL [numeric]	Mean (sd) : 14 (47.1) min < med < max: 0 < 0 < 621 IQR (CV) : 0 (3.4)	54 distinct values		5187 (91.2%)
143	HYDROXYPROLINE [numeric]	Mean (sd) : 0.1 (0.1) min < med < max: 0 < 0 < 0.7 IQR (CV) : 0.2 (1.3)	197 distinct values		5083 (89.3%)
144	FATTY ACIDS, SATURATED, 13:0 TRIDECANOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (10.7)	10 distinct values		5241 (92.1%)
145	FATTY ACIDS, POLYUNSATURATED, 21:5 [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.4 IQR (CV) : 0 (11.5)	12 distinct values		4734 (83.2%)
146	FATTY ACIDS, MONOUNSATURATED, 24:1undifferentiated, TETRACOSENOIC [numeric]	Mean (sd) : 0 (0.1) min < med < max: 0 < 0 < 3 IQR (CV) : 0 (19.8)	32 distinct values		4550 (80.0%)
147	FATTY ACIDS, MONOUNSATURATED, 12:1, LAUROLEIC [numeric]	1 distinct value	0 : 351 (100.0%)		5339 (93.8%)
148	FATTY ACIDS, POLYUNSATURATED, 22:3, [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.1 IQR (CV) : 0 (13.6)	10 distinct values		4754 (83.6%)
149	FATTY ACIDS, POLYUNSATURATED, 22:2, DOCOSADIENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0 IQR (CV) : 0 (15.5)	4 distinct values		4690 (82.4%)
150	FATTY ACIDS, POLYUNSATURATED, 18:2t,t , OCTADECADIENENOIC [numeric]	Mean (sd) : 0 (0) min < med < max: 0 < 0 < 0.5 IQR (CV) : 0 (5.9)	59 distinct values		4622 (81.2%)
151	CAMPESTEROL [numeric]	Mean (sd) : 3.8 (16) min < med < max: 0 < 0 < 189 IQR (CV) : 0 (4.2)	27 distinct values		5400 (94.9%)
152	BIOTIN [numeric]	Mean (sd) : 6.1 (6.5) min < med < max: 0 < 3.5 < 31.6 IQR (CV) : 7.2 (1.1)	71 distinct values		5585 (98.2%)
153	NA [numeric]	1 distinct value	1 distinct values		5689 (100.0%)
154	OXALIC ACID [numeric]	Mean (sd) : 0.3 (0.4) min < med < max: 0 < 0.1 < 1.7 IQR (CV) : 0.3 (1.4)	27 distinct values		5639 (99.1%)

Missing values in clean dataset

Citations for packages used

Analyses were conducted using the R Statistical language (version 3.6.0; R Core Team, 2019) on macOS Mojave 10.14.6, using the packages GGally (version 2.0.0; Barret Schloerke et al., 2020), gtsummary (version 1.3.6; Daniel Sjoberg et al., 2021), summarytools (version 0.9.8; Dominic Comtois, 2020), Matrix (version 1.2.17; Douglas Bates and Martin Maechler, 2019), RColorBrewer (version 1.1.2; Erich Neuwirth, 2014), ggplot2 (version 3.3.3; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.), tidyverse (version 1.2.1; Hadley Wickham, 2017), stringr (version 1.4.0; Hadley Wickham, 2019), tidyr (version 1.1.2; Hadley Wickham, 2020), forcats (version 0.5.1; Hadley Wickham, 2021), readr (version 1.3.1; Hadley Wickham, Jim Hester and Romain Francois, 2018), dplyr (version 1.0.2; Hadley Wickham et al., 2020), stargazer (version 5.2.2; Hlavac, Marek, 2018), wordcloud (version 2.6; Ian Fellows, 2018), tm (version 0.7.8; Ingo Feinerer and Kurt Hornik, 2020), glmnet (version 4.1.1; Jerome Friedman et al., 2010), car (version 3.0.3; John Fox and Sanford Weisberg, 2019), carData (version 3.0.2; John Fox, Sanford Weisberg and Brad Price, 2018), here (version 1.0.1; Kirill Müller, 2020), tibble (version 3.1.0; Kirill Müller and Hadley Wickham, 2021), NLP (version 0.2.1; Kurt Hornik, 2020), purrr (version 0.3.4; Lionel Henry and Hadley Wickham, 2020), sjPlot (version 2.8.6; Lüdecke D, 2020), report (version 0.3.0; Makowski et al., 2020), data.table (version 1.12.2; Matt Dowle and Arun Srinivasan, 2019), varhandle (version 2.0.5; Mehrad Mahmoudian, 2020), SnowballC (version 0.7.0; Milan Bouchet-Valat, 2020), imputeTS (version 3.2; Moritz S, Bartz-Beielstein T, 2017), pacman (version 0.5.1; Rinker et al., 2017), corrplot (version 0.84; Taiyun Wei and Viliam Simko, 2017) and pROC (version 1.17.0.1; Xavier Robin et al., 2011).

References

• Barret Schloerke, Di Cook, Joseph Larmarange, Francois Briatte, Moritz Marbach, Edwin Thoen, Amos Elberg and Jason Crowley (2020). GGally: Extension to ‘ggplot2’. R package version 2.0.0. https://CRAN.R-project.org/package=GGally
• Daniel D. Sjoberg, Michael Curry, Margie Hannum, Karissa Whiting and Emily C. Zabor (2021). gtsummary: Presentation-Ready Data Summary and Analytic Result Tables. R package version 1.3.6. https://CRAN.R-project.org/package=gtsummary
• Dominic Comtois (2020). summarytools: Tools to Quickly and Neatly Summarize Data. R package version 0.9.8. https://CRAN.R-project.org/package=summarytools
• Douglas Bates and Martin Maechler (2019). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.2-17. https://CRAN.R-project.org/package=Matrix
• Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer
• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
• Hadley Wickham (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse
• Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
• Hadley Wickham (2020). tidyr: Tidy Messy Data. R package version 1.1.2. https://CRAN.R-project.org/package=tidyr
• Hadley Wickham (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1. https://CRAN.R-project.org/package=forcats
• Hadley Wickham, Jim Hester and Romain Francois (2018). readr: Read Rectangular Text Data. R package version 1.3.1. https://CRAN.R-project.org/package=readr
• Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2020). dplyr: A Grammar of Data Manipulation. R package version 1.0.2. https://CRAN.R-project.org/package=dplyr
• Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2.1. https://CRAN.R-project.org/package=stargazer
• Ian Fellows (2018). wordcloud: Word Clouds. R package version 2.6. https://CRAN.R-project.org/package=wordcloud
• Ingo Feinerer and Kurt Hornik (2020). tm: Text Mining Package. R package version 0.7-8. https://CRAN.R-project.org/package=tm
• Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL https://www.jstatsoft.org/v33/i01/.
• John Fox and Sanford Weisberg (2019). An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/Books/Companion/
• John Fox, Sanford Weisberg and Brad Price (2018). carData: Companion to Applied Regression Data Sets. R package version 3.0-2. https://CRAN.R-project.org/package=carData
• Kirill Müller (2020). here: A Simpler Way to Find Your Files. R package version 1.0.1. https://CRAN.R-project.org/package=here
• Kirill Müller and Hadley Wickham (2021). tibble: Simple Data Frames. R package version 3.1.0. https://CRAN.R-project.org/package=tibble
• Kurt Hornik (2020). NLP: Natural Language Processing Infrastructure. R package version 0.2-1. https://CRAN.R-project.org/package=NLP
• Lionel Henry and Hadley Wickham (2020). purrr: Functional Programming Tools. R package version 0.3.4. https://CRAN.R-project.org/package=purrr
• Lüdecke D (2020). sjPlot: Data Visualization for Statistics in SocialScience. R package version 2.8.6, <URL:https://CRAN.R-project.org/package=sjPlot>.
• Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. Available from https://github.com/easystats/report. doi: .
• Matt Dowle and Arun Srinivasan (2019). data.table: Extension of data.frame. R package version 1.12.2. https://CRAN.R-project.org/package=data.table
• Mehrad Mahmoudian (2020). varhandle: Functions for Robust Variable Handling. R package version 2.0.5. https://CRAN.R-project.org/package=varhandle
• Milan Bouchet-Valat (2020). SnowballC: Snowball Stemmers Based on the C ‘libstemmer’ UTF-8 Library. R package version 0.7.0. https://CRAN.R-project.org/package=SnowballC
• Moritz S, Bartz-Beielstein T (2017). “imputeTS: Time Series MissingValue Imputation in R.” The R Journal, 9(1), 207-218. doi:10.32614/RJ-2017-009 (URL: https://doi.org/10.32614/RJ-2017-009).
• R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
• Taiyun Wei and Viliam Simko (2017). R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). Available from https://github.com/taiyun/corrplot

• Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez and Markus Müller (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, p. 77. DOI: 10.1186/1471-2105-12-77 http://www.biomedcentral.com/1471-2105/12/77/

• possibly use this to write functions

Another way to handle NAs

• remove variables with more than 50% of NAs + apply mean imputation
• applied the same analytic approach to compare the results with our main findings. would removing variables with a lot of NAs change the results?

1. Data Preparation

## [1] "Size of each cluster is" "9"                      
## [3] "21"                      "9"                      
## [5] "23"                      "97"                     
## [7] "1785"                    "3687"                   
## [9] "59"